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Space-borne observations for detecting and forecasting sea ice cover extremes


Provision of new observational products for initialization (SMOS+CRYOSAT2 thickness and along track sea ice concentration)

In order to take advantage of the higher temporal resolution of swath based sea ice products with accurate timestamps Level 2 data will be provided to the model partners for assimilation and validation purposes The actual production of the L2 data will be done in WPs 27First test version at KO12 and production version at KO24

Operational sea-ice freeboard and thickness data from synthetic aperture radar altimetry

An operational data product of sea ice thickness and freeboard from SAR altimetry data will be published. This data product will concentrate on regions of high interest, and it will provide highest possible spatial and temporal resolution.

Datasets of ice and snow parameters for ice thickness retrieval and for input to WP8

Estimates of snow and ice parameters from snap shots or time series of NWP and satellite data. Snow/ice parameters should include snow thickness, snow density and snow/ice interface temperature. The dataset should include Arctic wide coverage for the month of May during several years. First version at KO+18, and final at KO+33.

Novel sea ice classification algorithms for SAR images

Various existing algorithms for SAR based sea ice classification are further developed for dualpolarized Sentinel1 and Radarsat2 images The methods include nonlinear clustering algorithms multiplepolarization SAR segmentation algorithms as well as segmentation and classification algorithms based on segmentwise features

Dataset of snow (and related ice) parameters over sea ice along IMB buoy trajectories.

Time series of snowice parameters along buoy drift trajectories as ASCII files in ESA CCI RRDP format Parameters include as many as possible of snow thickness snow density ice thickness surface temperature icesnow interface temperature temperatures at standard levels in snow and iceFirst version at KO9 2nd version at KO21 and final at KO30

Dataset of snow (and related ice-) parameters from OIB and CryoVex campaigns

Time series of snowice parameters along ice drift trajectories as ASCII files in ESA CCI RRDP format Parameters include as many as possible of snow thickness snow density ice thickness surface temperature icesnow interface temperature temperatures at standard levels in snow and iceFirst version at KO9 and final at KO21

Pre-processing methods for dual-polarized Sentinel-1 IW/EM and RADARSAT-2 ScanSAR images

Description of methods and software for preprocessing of SAR eg georectifcation calibration incidence angle scaling filtering

Albedo and MPF data set

Production of albedo, MPF and ice concentration data sets, for at least three years, based on MERIS (2002-2012), AMSR-E and SMOS (starting on 2010) and starting on 2015 based on Sentinel-3 (optical) and AMSR2 and SMOS/SMAP observations.

Gridded product of SMOS and SMAP TB (daily average; resolution 12-15 km) and uncertainties

The Microwave Imagine Radiometer with Aperture Synthesis MIRAS aboard ESAs SMOS satellite measures the Earths surface brightness temperature TB at LBand frequency of 14 GHz NASAs SMAP spacecraft carries a 12 GHz radar and a 14 GHz radiometer that share a single feedhorn and a mesh reflector The synthetic aperture technique of SMOS allows to measure TB at a range of incidence angles while SMAP uses a conical scan geometry and a constant incidence angle at 40 In order to generate a homogeneous SMOSSMAP data product the SMOS TB will be interpolated to the SMAP incidence angle of 40 SMOS and SMAP polarized TBs and their estimated uncertainties will be projected into a common grid eg polar stereographic or EASE Data products will be generated using standard NetCDF format

Sea-ice freeboard, thickness from CryoSat-2 and snow-depth with weekly resolution

A new data product will be generated from the CryoSat-2 data. Differently from usual monthly products, a weekly product will be generated, including the frequent updates from orbit data.

Dataset of SAR based sea ice products

Set of SAR based sea ice products generated using the developed novel algorithms for utilization in other WPs. First version at KO+12, updated throughout to KO+18.

OE-tool for large scale sea ice and snow parameters from satellite and NWP data

Optimal estimation inversion tool to compute estimations of snow and ice parameters from time series of NWP and satellite data. Snow/ice parameters should include snow thickness, snow density and snow/ice interface temperature. First version at KO+8, and final at KO+30.

Gridded product of SMAP sigma-0 (daily average; resolution 1-3 km) and uncertainties

NASAs SMAP 12 GHz radar measures the normalized backscatter coefficient sigma0 at a high resolution 13 km over the Arctic Ocean A product of sigma0 values and their uncertainties will be defined as a grid compatible to the TB grid ie with the same projection as the SMOSSMAP TB Data products will be generated using standard NetCDF format

Adjusted sea ice classification methodology to satellite altimeter data based on existing airborne altimeter methodology

Earlier the possibility of sea ice classification using Airborne Synthetic Aperture and Interferometric Radar Altimeter System ASIRAS was demonstrated Significant differences between waveform shape parameters allowed to classify firstyearice and multiyear ice as well as leads by applying a Bayesian based method Further analyses are conducted to test how these results can be adapted to satellite borne altimeter systems

Arctic sea ice type product from satellite altimetry

This is a data product of sea ice type in digital format netcdf following CF convention based on radar altimeter data The product will be made freely available for the scientific community

Co-located dataset of daily data along buoy tracks for forward model development

Time series of satellite and ERA Interim NWP data colocated with the buoy and ice drift trajectories from D1.2 and D1.3. Satellite data should include as many as possible of AMSR, SMOS, ASCAT, IR, SMAP, OSCAT, SSMIS, Sentinel-1 and Cryosat. NWP data every 6 hours should include: air pressure [MSL; 151], 2 m air temperature [2T; 167], 10 m wind speed U [10U; 165], 10 m wind speed V [10V; 166], solar short wave incoming radiation [SSRD; 169], thermal longwave incoming radiation [STRD; 175], dewpoint-temp [2D; 168], Total precipitation (m) [TP; 228], TotalCloudLiquidWater [TCLW; 78], TotalCloudIceWater [TCIW; 79] and TotalCloudWaterVapour [TCWV; 137] First version at KO+12, 2nd at KO+24, and final at KO+36.

Assimilation of CryoSat-2 orbit data in seasonal forecast models

Description of methods for assimilating CryoSat-2 based estimations of e.g. sea ice typing and thickness along the satellite ground track (products from D7.1 ) into seasonal forecasting systems.

Retrieval methodology to retrieve applied WMO ice classes from RA data

A demonstration of retrieving sea ice type from radar altimeter data and the documentation of the methodology used

Gridded product of sea ice thickness from SMOS and SMAP and uncertainties

The operational SMOS algorithm of UHAM will be adjusted for the use with SMOS and SMAP TBs at a constant incidence angle The ice thickness and its uncertainty will be estimated from the TBs and delivered on the common grid Data products will be generated using standard NetCDF format

Improved mean sea-surface height product with near-real time availability

An intermediate product of CryoSat-2 data processing is a sea-surface height product. This will be extracted and made publicly available for various external applications, e.g. in oceanography.

Albedo and MPF retrieval methodology based on PM observations

Determination of the albedo and MPF retrievals based on PM observations.

Forward models for large scale sea ice and snow parameters from satellite and NWP data

Source code and documentation of model to compute time series of expected satellite signatures along ice drift trajectories from WP 1. Signatures should include at least TBs at AMSR and SMOS wavelengths and backscatter at C- and Ku-band. First version at KO+12, and final at KO+24.

Innovation Management and Service Development Plan

SPICES organizes thematic workshops for potential end-users, and participates in, both scientifical and industrial, workshops/meetings. Based on these SPICES Innovation Management and Service Plan is formed. First version at K0+12, updated at K0+24 and K0+36.

Report on SMOS and SMAP TB data quality and comparison

The quality of SMOS and SMAP TBs will be compared Potential biases between the different sensor products will be analysed The influence of error sources such as RFI will be investigated The uncertainty of SMOS and SMAP TBs will be estimated from time series over stable targets

Definition of new set of observation-based metrics relevant for regional applications, and corresponding verification dataset

This deliverable consists of a report that defines and evaluates a list of user-driven metrics that are useful for the evaluation of regional forecast performance. This implies also an evaluation of methods used for downscaling or upscaling of simulation output and the observational data sets.

Data Management Plan

Description of plans for management of Open Research Data (archiving, sharing, access, search, dissemination etc.) and data management within the SPICES project between the partners.

Plan for buoy deployments (including in-kind buoys from other projects)

The deployments of various buoy types need to be coordinated among the project partners and within international networks This coordination will mostly take place during the first three months and will be summarized in the report However this plan will be updated regularly over the entire project duration

Limit of sea ice thickness determination from SMOS in onset of melt

Describes limits of sea ice thickness determination from SMOS during the onset of melt

Influence of MPF on sea ice concentration retrieval

Determination of the influence of MPF on sea ice concentration retrieval using albedo and MPF data from existing retrievals in situ observation of melt ponds from Polarstern bridgeobservations and aerial images taken during EM Bird flights

Uncertainty analysis of CryoSat-2 orbit data of the fast-delivery-mode data product

CryoSat2 data products are usually released as gridded data averaging over multiple orbits and thus averaging over time Here we will assess the uncertainty of single orbit data though direct comparisons with field observations and in comparison with single orbits of the same region with minimal time offset

Report on 1.4 GHz sea ice thickness retrieval validation

SMOS and SMAP data products of sea ice thickness will be validated using airborne ice thickness measurements. The potential of SMAP polarized sigma-0 for surface classification and disaggregation will be evaluated. A strategy for the potential improvement of the ice thickness retrieval using a combination of active and passive L-band measurements will be described.

Evaluation of impact of new initialization on forecasts using new metrics

This deliverable consists of a report documenting the predictive skill (from weeks to months) of the sea-ice conditions and their impact of the atmosphere achieved by the ECMWF forecasting systems. The new observations and metrics resulting from SPICES will be used in the evaluation. The impact of selected SPICES-data sets on the initialization of sea-ice will be evaluated.

Final Report

Overview of major SPICES results and description of developed end-user products.

Statistical relation between (albedo and MPF) and brightness temperatures of PM sensors from 1.4 to 89 GHz

Statistical relation between albedo and MPF and brightness temperatures of PM sensors from 14 to 89 GHz with seasonal and regional dependences

Report on retrieval of sea ice thickness from the SAR wave-spectrum and validation

The procedure for the retrieval of sea-ice thickness will be applied to areas of frazil-pancake (FP) ice during periods of new ice formation and ice growth in regions of turbulence. Both ESA Sentinel-1 (S1) C-band and Cosmo-SkyMed (CSK) X-band SAR images, in areas of the Arctic (Greenland Sea) and of Antarctica (Ross Sea), will be used. A first phase of the study will focus on the development of a processing scheme for the automatic detection of FP ice fields and on the comparison of the results obtained with S1 and CSK images. By extracting a subset of the image across the ice edge, the SAR-wave spectra, both in ice and open sea, will be computed; these spectra will be used as input to a wave-ice interaction model to generate ice thicknesses. The results of this procedure will be validated with direct ice measurements performed during field campaigns carried out by other WPs of the project. The final deliverable will be seasonal pancake-frazil ice thicknesses (i.e. ice volume per unit sea surface area) and thus ice mass fluxes, for specific regions in which frazil-pancake ice is the dominant ice type. These will include: in the Antarctic the outer growing ice edge in early winter, and the Ross Sea and similar coastal polynyas throughout the year; in the Arctic the Odden ice tongue region and selected coastal polynyas in areas such as the Bering Sea coastline. In collaboration with UB, areas of thin ice will be selected where both SMOS and SAR imagery are available. UB will carry out SMOS retrievals yielding thickness values based on the SMOS algorithm, while UNIVPM and CNR will retrieve thicknesses using the pancake wave method. Results will be compared in an attempt to find a cross-correlation between SMOS and SAR in frazil-pancake ice regions.

Communication Activity Plan

Communication plans with potential end-users of the new products. Planning of two workshops for promoting new SPICES products and getting feedback and suggestions for improvement from the end-user community. SPICES publication plan (peer reviewed open access scientific publications, conferences and workshops). Outreach and promotion brochures addressed specifically to an end user group and to the wider scientific community. Key workshop forums that the SPICES project will commit to presenting its results and products. First version at KO+6, updated at KO+26.

Validation of SAR based sea ice products

Validation of SAR based sea ice products: input datasets, methods, results (e.g. relative and absolute accuracies).

Second Progress Report

SPICES results during the second year.

Comparison of sea ice type estimates from satellite radar altimetry and auxilliary sea ice type products

Comparison of the sea ice type classification results based on radar altimeter data and other sea ice type products such as the OSISAF sea ice type product available

Retrieval algorithm for albedo and MPF from Sentinel-3 observations

Transfer existing albedo and MPF retrieval algorithm based on MERIS to Sentinel3 including cloud screening

First Progress Report

SPICES results during the first year

Project web site

SPICES website for general introduction of SPICES objectives methods and output time table and products and dissemination of the products List of SPICES publications is also shownFirst version at KO3 updated throughout the project


Satellite Observations for Detecting and Forecasting Sea-Ice Conditions: A Summary of Advances Made in the SPICES Project by the EU’s Horizon 2020 Programme

Author(s): M. Mäkynen, J. Haapala, G. Aulicino, B. Balan-Sarojini, M. Balmaseda, A. Gegiuc, F. Girard-Ardhuin, S. Hendricks, G. Heygster, L. Istomina, L. Kaleschke, J. Karvonen, T. Krumpen, M. Lensu, M. Mayer, F. Parmiggiani, R. Ricker, E. Rinne, A. Schmitt, M. Similä, S. Tietsche, R. Tonboe, P. Wadhams, M. Winstrup, H. Zuo
Published in: Remote Sensing, Issue 12(7), 2020, ISSN 2072-4292
Publisher: MDPI
DOI: 10.3390/rs12071214

MODIS Sea Ice Thickness and Open Water–Sea Ice Charts over the Barents and Kara Seas for Development and Validation of Sea Ice Products from Microwave Sensor Data

Author(s): Marko Mäkynen; Juha Karvonen
Published in: Remote Sensing, Issue 9/12, 2017, Page(s) 1324, ISSN 2072-4292
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/rs9121324

Estimation of degree of sea ice ridging based on dual-polarized C-band SAR data

Author(s): Alexandru Gegiuc, Markku Similä, Juha Karvonen, Mikko Lensu, Marko Mäkynen, Jouni Vainio
Published in: The Cryosphere, Issue 12/1, 2018, Page(s) 343-364, ISSN 1994-0424
Publisher: Copernicus Publications
DOI: 10.5194/tc-12-343-2018

Satellite-observed drop of Arctic sea ice growth in winter 2015-2016

Author(s): Robert Ricker, Stefan Hendricks, Fanny Girard-Ardhuin, Lars Kaleschke, Camille Lique, Xiangshan Tian-Kunze, Marcel Nicolaus, Thomas Krumpen
Published in: Geophysical Research Letters, Issue 44/7, 2017, Page(s) 3236-3245, ISSN 0094-8276
Publisher: American Geophysical Union
DOI: 10.1002/2016GL072244

Synthetic aperture radar analysis of floating ice at Terra Nova Bay—an application to ice eddy parameter extraction

Author(s): Miguel Moctezuma-Flores, Flavio Parmiggiani, Corrado Fragiacomo, Lorenzo Guerrieri
Published in: Journal of Applied Remote Sensing, Issue 11/2, 2017, Page(s) 026041, ISSN 1931-3195
Publisher: Society of Photo-Optical Instrumentation Engineers
DOI: 10.1117/1.jrs.11.026041

Uncertainty reduction of Arctic sea ice freeboard from CryoSat-2 interferometric mode

Author(s): A. Di Bella, H. Skourup, J. Bouffard, T. Parrinello
Published in: Advances in Space Research, 2018, ISSN 0273-1177
Publisher: Pergamon Press Ltd.
DOI: 10.1016/j.asr.2018.03.018

Pancake Ice Thickness Mapping in the Beaufort Sea From Wave Dispersion Observed in SAR Imagery

Author(s): P. Wadhams, G. Aulicino, F. Parmiggiani, P. O. G. Persson, B. Holt
Published in: Journal of Geophysical Research: Oceans, Issue 123/3, 2018, Page(s) 2213-2237, ISSN 2169-9275
Publisher: American Geophysical Union
DOI: 10.1002/2017jc013003

The color of melt ponds on Arctic sea ice

Author(s): Peng Lu, Matti Leppäranta, Bin Cheng, Zhijun Li, Larysa Istomina, Georg Heygster
Published in: The Cryosphere, Issue 12/4, 2018, Page(s) 1331-1345, ISSN 1994-0424
Publisher: Copernicus Publications
DOI: 10.5194/tc-12-1331-2018

Incidence Angle Dependence of First-Year Sea Ice Backscattering Coefficient in Sentinel-1 SAR Imagery Over the Kara Sea

Author(s): Marko Makynen, Juha Karvonen
Published in: IEEE Transactions on Geoscience and Remote Sensing, Issue 55/11, 2017, Page(s) 6170-6181, ISSN 0196-2892
Publisher: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tgrs.2017.2721981

Tracking of the iceberg created by the Nansen Ice Shelf collapse

Author(s): M. Moctezuma-Flores, F. Parmiggiani
Published in: International Journal of Remote Sensing, Issue 38/5, 2017, Page(s) 1224-1234, ISSN 0143-1161
Publisher: Taylor & Francis
DOI: 10.1080/01431161.2016.1275054

A Consistent Combination of Brightness Temperatures from SMOS and SMAP over Polar Oceans for Sea Ice Applications

Author(s): Amelie Schmitt, Lars Kaleschke
Published in: Remote Sensing, Issue 10/4, 2018, Page(s) 553, ISSN 2072-4292
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/rs10040553

Estimation of Arctic land-fast ice cover based on dual-polarized Sentinel-1 SAR imagery

Author(s): Juha Karvonen
Published in: The Cryosphere, Issue 12/8, 2018, Page(s) 2595-2607, ISSN 1994-0424
Publisher: Copernicus Publications
DOI: 10.5194/tc-12-2595-2018

Image processing for pancake ice detection and size distribution computation

Author(s): F. Parmiggiani, M. Moctezuma-Flores, P. Wadhams, G. Aulicino
Published in: International Journal of Remote Sensing, 2018, Page(s) 1-16, ISSN 0143-1161
Publisher: Taylor & Francis
DOI: 10.1080/01431161.2018.1541367

Satellite-derived sea ice export and its impact on Arctic ice mass balance

Author(s): Robert Ricker, Fanny Girard-Ardhuin, Thomas Krumpen, Camille Lique
Published in: The Cryosphere, Issue 12/9, 2018, Page(s) 3017-3032, ISSN 1994-0424
Publisher: Copernicus Publications
DOI: 10.5194/tc-12-3017-2018

Year-round impact of winter sea ice thickness observations on seasonal forecasts

Author(s): B. Balan-Sarojini, S. Tietsche, M. Mayer, M. Balmaseda, H. Zuo, P. de Rosnay, T. Stockdale, F. Vitart
Published in: The Cryosphere, Issue 15, 2021, Page(s) 325–344, ISSN 1994-0416
Publisher: Copernicus Publications
DOI: 10.5194/tc-15-325-2021

Identifying pancake ice and computing pancake size distribution in aerial photographs

Author(s): Miguel Moctezuma-Flores, Fiorigi F. Parmiggiani, Lorenzo Guerrieri
Published in: Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2017, 2017, Page(s) 22, ISBN 9781-510613096
Publisher: SPIE
DOI: 10.1117/12.2277537

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